Towards Safe Maneuvering of Double-Ackermann-Steering Robots with a Soft Actor-Critic Framework
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Abstract (arXiv)
We present a deep reinforcement learning framework based on Soft Actor-Critic (SAC) for safe and precise maneuvering of double-Ackermann-steering mobile robots (DASMRs). Unlike holonomic or simpler non-holonomic robots such as differential-drive robots, DASMRs face strong kinematic constraints that make classical planners brittle in cluttered environments. Our framework leverages the Hindsight Experience Replay (HER) and the CrossQ overlay to encourage maneuvering efficiency while avoiding obstacles. Simulation results with a heavy four-wheel-steering rover show that the learned policy can robustly reach up to 97% of target positions while avoiding obstacles. Our framework does not rely on handcrafted trajectories or expert demonstrations.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 18 references that are in the map. First posted on arXiv 11 Oct 2025.
Builds on
10 of 18 references.
References · 10
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2017Hindsight Experience Replaycited by 1,096
- 2018On Evaluation of Embodied Navigation Agentscited by 526
- 2019CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicitycited by 89
- 2022Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shapingcited by 8
- 2024FRASA: An End-to-End Reinforcement Learning Agent for Fall Recovery and Stand Up of Humanoid Robotscited by 8
- 2022Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogisticscited by 3
- 2023Multi Actor-Critic DDPG for Robot Action Space Decomposition: A Framework to Control Large 3D Deformation of Soft Linear Objectscited by 3
- 2024FootstepNet: an Efficient Actor-Critic Method for Fast On-line Bipedal Footstep Planning and Forecastingcited by 3
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Citing papers per year: 2026: 2.
2 papers.